Statistical Modeling of Fishing Activities in the North Atlantic

This paper deals with the issue of modeling daily catches of fishing boats in the Grand Bank fishing grounds. We have data on catches per species for a number of vessels collected by the European Union in the context of the North Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics ---such as the size of the ship, the fishing technique used, the mesh size of the nets, etc.---, are obvious candidates, but one can also consider the season or the actual location of the catch. In all, our database leads to 23 possible regressors, resulting in a set of $8.4\times 10^6$ possible linear regression models. Prediction of future catches and posterior inference will be based on Bayesian model averaging, using a Markov Chain Monte Carlo Model Composition (MC$^3$) approach. Particular attention is paid to the elicitation of the prior and the prediction of catch for single and aggregated observations.